Projecting Urbanization and Landscape Change at Large Scale Using the FUTURES Model

Derek Van Berkel, Ashwin Shashidharan, Rua S. Mordecai, Raju Vatsavai, Anna Petrasova, Vaclav Petras, Helena Mitasova, John B. Vogler and Ross K. Meentemeyer
Additional contact information
Derek Van Berkel: Center for Geospatial Analytics, North Carolina State University, Raleigh, NC 27695, USA
Ashwin Shashidharan: Center for Geospatial Analytics, North Carolina State University, Raleigh, NC 27695, USA
Rua S. Mordecai: U.S. Fish & Wildlife Service, South Atlantic Landscape Conservation Cooperative, Raleigh, NC 27699, USA
Raju Vatsavai: Center for Geospatial Analytics, North Carolina State University, Raleigh, NC 27695, USA
Anna Petrasova: Center for Geospatial Analytics, North Carolina State University, Raleigh, NC 27695, USA
Vaclav Petras: Center for Geospatial Analytics, North Carolina State University, Raleigh, NC 27695, USA
Helena Mitasova: Center for Geospatial Analytics, North Carolina State University, Raleigh, NC 27695, USA
John B. Vogler: Center for Geospatial Analytics, North Carolina State University, Raleigh, NC 27695, USA
Ross K. Meentemeyer: Center for Geospatial Analytics, North Carolina State University, Raleigh, NC 27695, USA

Land, 2019, vol. 8, issue 10, 1-28

Abstract: Increasing population and rural to urban migration are accelerating urbanization globally, permanently transforming natural systems over large extents. Modelling landscape change over large regions, however, presents particular challenges due to local-scale variations in social and environmental factors that drive land change. We simulated urban development across the South Atlantic States (SAS), a region experiencing rapid population growth and urbanization, using FUTURES—an open source land change model that uses demand for development, local development suitability factors, and a stochastic patch growing algorithm for projecting alternative futures of urban form and landscape change. New advances to the FUTURES modelling framework allow for high resolution projections over large spatial extents by leveraging parallel computing. We simulated the adoption of different urban growth strategies that encourage settlement densification in the SAS as alternatives to the region’s increasing sprawl. Evaluation of projected patterns indicate a 15% increase in urban lands by 2050 given a status quo development scenario compared to a 14.8% increase for the Infill strategy. Status quo development resulted in a 3.72% loss of total forests, 2.97% loss of highly suitable agricultural land, and 3.69% loss of ecologically significant lands. An alternative Infill scenario resulted in similar losses of total forest (3.62%) and ecologically significant lands (3.63%) yet consumed less agricultural lands (1.23% loss). Moreover, infill development patterns differed qualitatively from the status quo and resulted in less fragmentation of the landscape.

Keywords: land change model; urbanization; parallel computing; ecosystem services; land sharing; land sparing (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
https://www.mdpi.com/2073-445X/8/10/144/pdf (application/pdf)
https://www.mdpi.com/2073-445X/8/10/144/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jlands:v:8:y:2019:i:10:p:144-:d:270144

Access Statistics for this article

Land is currently edited by Ms. Carol Ma

More articles in Land from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().